A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.
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2. The system of claim 1, wherein the one or more parameters are raw data for the component of the additive manufacturing device.
3. The system of claim 1, wherein the component is a cathode, and the raw data includes a grid voltage and a beam current for the cathode.
This invention relates to a system for monitoring and analyzing components in an electron beam device, such as an electron microscope or particle accelerator. The system addresses the challenge of detecting and diagnosing performance issues in critical components, particularly cathodes, by collecting and processing raw operational data to identify deviations from expected behavior. The system includes a data acquisition module that collects raw data from the cathode, specifically the grid voltage and beam current, which are key indicators of cathode performance. These parameters are monitored in real-time or near real-time to detect anomalies that may indicate degradation, misalignment, or other operational faults. The system also includes an analysis module that processes the raw data to generate performance metrics, such as stability, efficiency, and consistency of the beam current relative to the grid voltage. These metrics are compared against predefined thresholds or historical data to determine whether the cathode is functioning within acceptable limits. Additionally, the system may include a diagnostic module that provides alerts or recommendations based on the analysis, allowing for proactive maintenance or adjustments to prevent failures. The system may also store historical data for trend analysis, enabling long-term monitoring of component performance and predictive maintenance. This approach improves the reliability and efficiency of electron beam devices by ensuring early detection of potential issues before they escalate into critical failures.
4. The system of claim 1, wherein the component is a cathode, and the physics features include at least one of a cathode temperature, a cathode brightness, and vacuum environment.
5. The system of claim 1, wherein the one or more transfer functions is updated based on parameters related to wear and tear of the component of the additive manufacturing device.
6. The system of claim 1, wherein the second module is configured to update the one or more classifiers based on parameters related to wear and tear of the component of the additive manufacturing device.
7. The system of claim 1, wherein the one or more classifiers include threshold values determined based on machine learning or statistical models on evaluation of builds and parameters for the builds.
The system relates to software build analysis, specifically improving the accuracy of build classification. In software development, builds often fail or produce errors, and identifying the root causes efficiently is critical for maintaining productivity. Traditional methods rely on manual inspection or simple rule-based systems, which are time-consuming and may miss subtle patterns in build failures. The system includes one or more classifiers that analyze software builds and their associated parameters to determine whether a build is likely to succeed or fail. These classifiers are trained using machine learning or statistical models, which evaluate historical build data to establish threshold values. The threshold values represent decision boundaries that distinguish between successful and failed builds based on learned patterns in the data. By applying these thresholds, the system can predict build outcomes with higher accuracy than rule-based approaches. The classifiers may also adapt over time as new build data is processed, improving their predictive performance. This automated approach reduces the need for manual intervention and speeds up the debugging process, allowing developers to focus on resolving critical issues rather than analyzing build logs. The system is particularly useful in continuous integration and deployment pipelines, where rapid feedback on build health is essential.
8. The system of claim 1, further comprising a fourth module configured to determine a cause for a failure of the component based on a comparison of the generated physics features of the first module and the one or more classifiers.
10. The method of claim 9, wherein the one or more parameters are raw data for the component of the additive manufacturing device.
11. The method of claim 9, wherein the component is a cathode, and the raw data includes a grid voltage and a beam current for the cathode.
12. The method of claim 9, wherein the component is a cathode, and the physics features include at least one of a cathode temperature, a cathode brightness, and vacuum environment.
This invention relates to monitoring and controlling physics features of a cathode in a vacuum environment, such as those used in electron beam devices. The cathode is a critical component in such systems, and its performance is influenced by factors like temperature, brightness, and the surrounding vacuum conditions. The invention provides a method to measure and adjust these physics features to optimize cathode operation. The method involves detecting one or more of the cathode's temperature, brightness, and vacuum environment parameters. These measurements are then used to determine whether adjustments are needed to maintain or improve cathode performance. If adjustments are required, the method includes modifying the cathode's operating conditions, such as altering the temperature or vacuum level, to achieve the desired performance. The goal is to ensure stable and efficient cathode operation, which is essential for the reliability and longevity of electron beam devices. This approach helps prevent degradation or failure of the cathode due to suboptimal conditions, thereby enhancing the overall system's functionality.
13. The method of claim 9, further comprising updating the one or more transfer functions based on parameters related to wear and tear of the component of the additive manufacturing device.
Additive manufacturing devices, such as 3D printers, often require precise control of material deposition to ensure part quality. Over time, components like nozzles, extruders, or laser systems degrade due to wear and tear, leading to inconsistencies in material flow, layer adhesion, or dimensional accuracy. This degradation can result in defective parts, reduced efficiency, and increased material waste. To address this, a method involves dynamically adjusting transfer functions used to control the additive manufacturing process. Transfer functions define relationships between input parameters (e.g., temperature, pressure, laser power) and output behaviors (e.g., material flow rate, layer thickness). By monitoring wear-related parameters—such as nozzle diameter changes, extruder motor torque, or laser beam intensity—these transfer functions are updated in real time. For example, if a nozzle wears down, the transfer function may adjust the extrusion pressure to compensate for the reduced flow rate. Similarly, if a laser system degrades, the transfer function may increase power to maintain consistent energy delivery. This adaptive approach ensures that the additive manufacturing process remains accurate and efficient despite component degradation, reducing defects and improving part quality. The method may also incorporate predictive models to anticipate wear and proactively adjust parameters before failures occur. By continuously refining the transfer functions based on real-time wear data, the system maintains optimal performance throughout the device's operational life.
14. The method of claim 9, further comprising updating the one or more classifiers based on parameters related to wear and tear of the component of the additive manufacturing device.
This invention relates to additive manufacturing systems, specifically improving the accuracy and reliability of component monitoring and maintenance. Additive manufacturing devices, such as 3D printers, often experience wear and tear on critical components like nozzles, extruders, or build platforms, which can degrade print quality over time. The invention addresses this by continuously updating machine learning classifiers that predict component performance and failure risks. These classifiers analyze real-time operational data, such as temperature fluctuations, material flow rates, or mechanical vibrations, to detect early signs of degradation. The system further enhances accuracy by incorporating wear and tear parameters—such as usage hours, material abrasion rates, or thermal cycling effects—into the classifier training process. By dynamically adjusting the classifiers based on these factors, the system ensures more precise predictions of component lifespan and maintenance needs, reducing downtime and improving print consistency. The approach integrates sensor data, historical performance records, and predictive modeling to create a self-improving maintenance framework for additive manufacturing devices.
15. The method of claim 9, wherein the one or more classifiers include threshold values determined based on machine learning or statistical models on evaluation of builds and parameters for the builds.
16. The method of claim 9, further comprising determining a cause for a failure of the component based on a comparison of the generated physics features and the one or more classifiers.
18. The non-transitory machine readable media of claim 17, wherein the one or more parameters are raw data for the component of the additive manufacturing device.
Additive manufacturing systems require precise control of machine parameters to ensure consistent and high-quality part production. Raw data from machine components, such as temperature sensors, pressure gauges, or motion controllers, is critical for monitoring and adjusting manufacturing processes in real time. However, managing and analyzing this raw data efficiently remains a challenge, particularly in distributed or automated systems where manual intervention is impractical. The invention provides a non-transitory machine-readable medium containing instructions for processing raw data from additive manufacturing device components. The system collects raw data directly from sensors or controllers associated with the machine, such as temperature readings, pressure values, or positional data. This data is then processed to optimize manufacturing operations, such as adjusting build parameters, detecting anomalies, or predicting maintenance needs. The medium may also include instructions for storing, transmitting, or analyzing the raw data to improve system performance. By leveraging raw component data, the invention enables more accurate process control, reducing defects and increasing production efficiency. The solution is particularly useful in automated or high-throughput additive manufacturing environments where real-time data processing is essential.
19. The non-transitory machine readable media of claim 17, wherein the component is a cathode, and the raw data includes a grid voltage and a beam current for the cathode.
20. The non-transitory machine readable media of claim 17, wherein the computer executable instructions, when executed by one or more processors, are configured to update the one or more transfer functions based on parameters related to wear and tear of the component of the additive manufacturing device.
Additive manufacturing systems require precise control of material deposition to ensure part quality. Over time, components such as nozzles, extruders, or print heads degrade due to wear and tear, affecting performance. This degradation can lead to inconsistencies in material flow, layer thickness, or dimensional accuracy, reducing print quality and reliability. To address this, a system monitors and compensates for component wear in real-time. The system includes a non-transitory machine-readable medium storing executable instructions that, when run by a processor, adjust transfer functions governing material deposition. These transfer functions define relationships between input parameters (e.g., temperature, pressure, feed rate) and output behaviors (e.g., extrusion rate, layer thickness). The system updates these functions dynamically based on wear-related parameters, such as nozzle diameter reduction, extruder motor torque fluctuations, or thermal degradation of print heads. By continuously recalibrating the transfer functions, the system maintains consistent material deposition despite component degradation, improving part accuracy and reducing waste. The system may also integrate sensor data (e.g., flow rate sensors, thermal imaging) to detect wear patterns and predict failure thresholds. Adjustments can be made proactively, extending component lifespan and minimizing downtime. This approach ensures high-quality additive manufacturing by compensating for wear-induced performance variations.
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July 27, 2021
November 22, 2022
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